航 空 发 动 机 压 气 机 叶 片 振 动 状 态 在 线 监 测 指 标

Blade tip timing (BTT) technology is an important approach for online monitoring of blade vibration. To address the problems of high computational cost， poor real-time performance， and strict operating-condition requirements in existing methods for distinguishing synchronous and asynchronous vibrations，this paper proposes an online identification method for blade vibration states based on time-domain indicators. Using only the displacement signal from a single BTT sensor，the mean and standard deviation indicators are constructed，and the vibration state is identified in real time according to the differences in the distribution characteristics of the sampled displacement sequences under synchronous and asynchronous vibrations. The sensitivity of the two indicators is analyzed through theoretical derivation， and the influence of window length on the indicator fluctuation curves，together with the criteria for window-length selection, is discussed. Simulated synchronous and asynchronous vibration signals are constructed to emulate BTT sampling，and the identification performance of the proposed indicators under different window lengths is verified and compared with that of conventional time-domain indicators，such as crest factor and kurtosis. BTT experiments are carried out on a compressor test rig to further validate the effectiveness of the proposed method. Simulation and experimental results show that， under an appropriate window length， the mean curve fluctuates significantly while the standard deviation curve remains stable during synchronous vibration， whereas the mean curve approaches zero and the standard deviation curve increases significantly during asynchronous vibration. Based on this，the blade vibration state can be accurately identified. The proposed method requires neither spectral analysis nor undersampled signal reconstruction，has low computational cost，and is suitable for online monitoring.